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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106674


    Title: CAD Model-based 3D Object Pose Estimation using an Edge-Based Nonlinear Model Fitting Algorithm
    Authors: Tsai, Chi-Yi;Wang, Wei-Yi;Huang, Chi-Hung;Shih, Bo-Ren
    Keywords: CAD model-based tracking;object pose estimation;model fitting;nonlinear optimization.
    Date: 2015/09/05
    Issue Date: 2016-04-27 11:20:40 (UTC+8)
    Publisher: IIAE
    Abstract: This paper addresses the design of a model-based 3D
    object pose estimation algorithm, which is one of the major
    techniques to develop a robust robotic vision system using a
    monocular camera. The proposed system first extracts line
    features of a captured image by using edge detection and
    Hough transform techniques. Given a CAD model of the
    object-of-interest, the 6-DOF pose of the object can then be
    estimated via a novel edge-based nonlinear model fitting
    algorithm, which is a nonlinear optimization process for
    estimating the optimal object pose based on an edge-based
    distance metric. Experimental results validate the
    performance of the proposed system.
    Relation: Proceedings of the 3rd IIAE International Conference on Intelligent Systems and Image Processing 2015, pp.59-62
    DOI: 10.12792/icisip2015.014
    Appears in Collections:[電機工程學系暨研究所] 會議論文

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